IN24A-01
An Advanced Next Generation Archival and Distribution System for Global Atmospheric Science Research and Applications
NASA's Atmospheric Science Data Center at the NASA Langley Research Center has developed a new state-of- the-art data archival, and distribution system to serve the atmospheric sciences data provider and user communities. The new system, called Archive – Next Generation (ANGe), is replacing two large-scale science data management systems, and is designed with a distributed, multi-tier, serviced-based, message oriented architecture enabling new methods for searching, accessing, and customizing data. The previous two systems required a user to actively manage a session in a web browser to sequentially search for and obtain data. The ANGe system is architected to allow programmatic calls to the archive via web services to obtain multiple data sets of interest to the user. Web service access to the archive enhances the user's ability to utilize multiple data sets managed at different locations via a Grid computing environment. This technology distributes computationally intensive data processing for large data sets, and greatly improves the efficiency of extracting smaller pieces of data of interest to a specific study. Geospatial metadata is managed in a PostGIS-enabled database, allowing for integration with mainstream GIS utilities and applications. The Atmospheric Science Data Center is also producing custom value-added data products and tailoring access to information and data to meet the needs of a diverse user community. Details of these new data access tools and capabilities, and planned enhancements will be discussed. The Atmospheric Science Data Center in Langley's Science Directorate leads NASA's program for the processing, archival and distribution of Earth science data in the areas of radiation budget, clouds, aerosols, and tropospheric chemistry. The Data Center was established in 1991 to support NASA's Earth Observing System and the U.S. Global Change Research Program. It is unique among NASA data centers in the size of its archive, cutting edge computing technology, and full range of data services. http://eosweb.larc.nasa.gov
IN24A-02
Solutions for Mining Distributed Scientific Data
Researchers at the University of Alabama in Huntsville (UAH) and the Goddard Earth Sciences Data and Information Services Center (GES DISC) are working on approaches and methodologies facilitating the analysis of large amounts of distributed scientific data. Despite the existence of full-featured analysis tools, such as the Algorithm Development and Mining (ADaM) toolkit from UAH, and data repositories, such as the GES DISC, that provide online access to large amounts of data, there remain obstacles to getting the analysis tools and the data together in a workable environment. Does one bring the data to the tools or deploy the tools close to the data? The large size of many current Earth science datasets incurs significant overhead in network transfer for analysis workflows, even with the advanced networking capabilities that are available between many educational and government facilities. The UAH and GES DISC team are developing a capability to define analysis workflows using distributed services and online data resources. We are developing two solutions for this problem that address different analysis scenarios. The first is a Data Center Deployment of the analysis services for large data selections, orchestrated by a remotely defined analysis workflow. The second is a Data Mining Center approach of providing a cohesive analysis solution for smaller subsets of data. The two approaches can be complementary and thus provide flexibility for researchers to exploit the best solution for their data requirements. The Data Center Deployment of the analysis services has been implemented by deploying ADaM web services at the GES DISC so they can access the data directly, without the need of network transfers. Using the Mining Workflow Composer, a user can define an analysis workflow that is then submitted through a Web Services interface to the GES DISC for execution by a processing engine. The workflow definition is composed, maintained and executed at a distributed location, but most of the actual services comprising the workflow are available local to the GES DISC data repository. Additional refinements will ultimately provide a package that is easily implemented and configured at additional data centers for analysis of additional science data sets. Enhancements to the ADaM toolkit allow the staging of distributed data wherever the services are deployed, to support a Data Mining Center that can provide additional computational resources, large storage of output, easier addition and updates to available services, and access to data from multiple repositories. The Data Mining Center case provides researchers more flexibility to quickly try different workflow configurations and refine the process, using smaller amounts of data that may likely be transferred from distributed online repositories. This environment is sufficient for some analyses, but can also be used as an initial sandbox to test and refine a solution before staging the execution at a Data Center Deployment. Detection of airborne dust both over water and land in MODIS imagery using mining services for both solutions will be presented. The dust detection is just one possible example of the mining and analysis capabilities the proposed mining services solutions will provide to the science community. More information about the available services and the current status of this project is available at http://www.itsc.uah.edu/mws/ http://www.itsc.uah.edu/mws/
IN24A-03
The SEAMONSTER Sensor Web: Lessons and Opportunities after One Year
The SouthEast Alaska MOnitoring Network for Science, Telecommunications, Education, and Research, or SEAMONSTER, is a NASA Earth Science Technology Office funded effort to deploy a sensor web in Southeast Alaska. One of the major benefits of this project is the potential for testbed applications for sensor web and sensor technologies is a harsh yet accessible environment. Another key aspect of SEAMONSTER is the project's illustration of the key differences between a sensor network and a sensor web. After the initial year of work on the project, we have instrumented the partially glaciated watershed of Lemon Creek, near Juneau, Alaska. The initial goal of this project is to develop a sensor web for monitoring the Lemon Glacier and its outlet stream, Lemon Creek. The sensor web is built upon a network of sensors with real time communication between nodes and semi-autonomous reconfigurability based on the information shared between nodes. The sensor web is designed to provide long term monitoring that is sensitive to local conditions to accurately record transient events with dynamic use of available resources (e.g. power, storage, communications bandwidth). Specifically, the sensor web described in this presentation allows us to develop our understanding of glacier hydrology and the influence of glacial runoff on the hydrology and hydrochemistry of Lemon Creek. We currently have 7 different stations monitoring 37 physical parameters. We are implementing communications via wireless 802.11b to transmit data from sensor web nodes back to the University of Alaska Southeast. The backbone of the sensor web is composed of Vexcel Microservers. These low-power servers are base stations with support for sub-networks, server support with respect to the rest of the network, and server behavior with respect to network-external contact. This presentation describes the methods we have used to build the SEAMONSTER sensor web, lessons learned after one year, future directions for the sensor web, and illustrate and solicit collaborations to utilize the testbed potentials of SEAMONSTER. http://seamonsterak.com/
IN24A-04
Development of Autonomous, Robotic Meteorological Stations for Improved Ice Sheet Climate Monitoring.
Continental Ice Sheets such as Antarctica and Greenland play a major role in the stability of the Earth's climate. These large ice masses are sensitive to changes in global temperatures and have an impact of planetary radiation balance, climate, and sea level. Investigating spatio-temporal variations of ice shelf surface energy balance at moderate scales ranging between tens of meters to kilometers would greatly enhance detection and long-term monitoring of the onset, duration, and magnitude of vigorous melt dynamics. Currently, automatic weather station (AWS) units are deployed in remote regions throughout Greenland and Antarctica. AWS units measure air temperature, wind speed and direction, air pressure, relative humidity. We have developed a prototype system consisting of a dynamic mast apparatus, meteorlogical instrument package, and track platform. A distributed network of these units that are relatively cheap, wireless capable, could provided am adaptive moderate scale monitoring system designed to augment the existing AWS network. Such a network would greatly improve our capacity as ice sheet scientists to assess the stability of these vital structures. http://www.geog.psu.edu/news/lampkinupdate.html
IN24A-05
Semantic Mediation and Integration of Volcanic and Atmospheric Data:In Search of Statistical Signatures
We present current results for a research effort into the application of semantic web methods and technologies to address the challenging problem of integrating heterogeneous volcanic and atmospheric data in support of assessing the atmospheric effects of a volcanic eruption. This scenario highlights what is true for the vast majority of data intensive Earth system investigations which have limited ability to explore important and difficult problems. This is because they are forced to find and use data representing an event or phemenonon of interest through data collections at the data-element, or syntactic, level rather than at a higher scientific, or semantic, level. The volcano eruption scenario exemplifies these challenges. We present how semantic web methodologies are implemented within existing distributed technology frameworks to provide essential, re-useable, and robust, support necessary for interdisciplinary scientific research activities. This project: Semantically-Enabled Science Data Integration (SESDI) is an NASA/ESTO/ACCESS-funded project involving the High Altitude Observatory at the National Center for Atmospheric Research (NCAR), McGuinness Associates Consulting, NASA/JPL and Virginia Polytechnic University.
IN24A-06
Goal-Directed Planning for Sensor Webs
An Earth-observing sensor web is an organization of space, airborne, or in situ sensing devices for collecting measurements of the Earth's processes. Sensor web coordination involves formulating Earth science goals and transforming them into sensor web workflows, i.e., sequences of data acquisition and processing tasks that satisfy the specified goals. Automating parts of this process using recent advances in intelligent control software technology will offer improved sensor web effectiveness. Our approach to the coordination problem applies architectural concepts of workflow management systems by identifying two phases in workflow generation. In the first phase, users formulate high-level campaign goals that are automatically transformed into abstract workflow plans. An abstract workflow plan represents the organization of data acquisition and processing actions that fulfills the goals specified by the user, but leaves out details such as how requests for access to a data resource are formatted. Abstracting away these details improves the usability of sensor web resources by scientists. To implement the first phase, we utilize the Labeled Transition System Analyzer (LTSA), a model-checking software tool. LTSA contains a concise process-based language, FSP (Finite State Processes) for designing and modeling software programs. We will use LTSA and FSP to automate the process of building executable plans for accessing resources on a sensor web. FSP has the constructs for representing conditional dependencies, iterations, and parallel actions, all of which are common features in Earth science campaigns. The second phase of the process consists of the automatic transformation of an abstract plan into a concrete plan, i.e., a sequence of actions that can be autonomously executed on a sensor web. The transformation in phase two might require further decomposition of actions in the abstract plan into a sequence of lower-level data acquisition requests. It may also involve the selection of resources to accomplish a given action and the representation of data acquisition tasks in a format that is recognized by the targeted resource (e.g. a sensor control command or a data archive query). The second phase relies on a service-layer information infrastructure for accessing sensor web resources. Standardizing requirements for such a service layer through the Open Geospatial Consortium Sensor Web Enablement (OGC/SWE) effort should allow access to numerous and diverse sensor web resources. For the purpose of demonstrating a prototype of our workflow management concepts, our system currently utilizes a simpler information infrastructure layer for servicing requests. This layer controls access to TOPS (Terrestrial Observation and Prediction System), a modeling software system that brings together technologies in information technology, weather/climate forecasting, ecosystem modeling, and satellite remote sensing to enhance management decisions related to floods, droughts, forest fires, human health, and crop, range, and forest production. We provide examples of concrete plans for accessing TOPS data and modeling resources and how they are generated from abstract plans.
IN24A-07
Initial Analyses and Demonstration of a Soil Moisture Smart Sensor Web Using Data Assimilation and Optimal Control
We have developed a new concept for a smart sensor web technology for measurements of soil moisture that include spaceborne and in-situ assets. The objective of the technology is to enable a guided/adaptive sampling strategy for the in-situ sensor network to meet the measurement validation objectives of the spaceborne sensors with respect to resolution and accuracy. The sensor nodes are guided to perform as a macro-instrument measuring processes at the scale of the satellite footprint, hence meeting the requirements for the difficult problem of validation of satellite measurements. The science measurement considered is the surface-to-depth profiles of soil moisture estimated from satellite radars and radiometers, with calibration and validation using in- situ sensors. Satellites allow global mapping but with coarse footprints. The total variability in soil-moisture fields comes from variability in processes on various scales. Installing an in-situ network to sample the field for all ranges of variability is impractical. However, a sparser but smarter network can provide the validation estimates by operating in a guided fashion with guidance from its own sparse measurements. The feedback and control take place in the context of a dynamic data assimilation system. The overall design of the smart sensor web including the control architecture, assimilation framework, and actuation hardware will be presented in this paper. The results of initial numerical and laboratory demonstrations of the sensor web concept, which includes a small number of soil moisture sensors and their physical measurement model, a dynamic soil moisture time-evolution model (SWAP), and an optimal control strategy will then be shown.
IN24A-08
Sensor Processing and Acquisition Network for Environmental Observation Systems
Technology advancements in sensor networks enable the development of long-term, in-situ observation systems to support earth sciences and ecological research. This work describes a major effort at USC/ISI in developing the Sensor Processing and Acquisition Network (SPAN), a core technology to build observation systems for a wide range of scientific applications. We will present the SPAN architecture, explain its major components, and show examples of a few deployed prototype systems. Our design decisions for SPAN emphasize important system properties such as robustness, flexibility, ease of use, and extensibility. First, to support long-term deployments, SPAN employs a rugged data acquisition platform, such as the CompactRIO from National Instruments, which provides robust sensor data collection under various environmental conditions. We developed command and status protocols that allow monitoring the system status and handling potential component failures. In addition, our data protocol provides a complete solution on reliable data buffering, transfer, and archiving. Second, the SPAN system is very flexible and supports different types of analog and digital sensors, which are used by different application domains. We have designed data management protocols that provide simple and unified interfaces for different types of sensors. Third, SPAN is designed to be used by scientists who may not always be networking or software experts. Our system provides comprehensive tools, with intuitive user interfaces so that scientists can control data sampling activity, monitor the system health, and reconfigure system components. Finally, we have designed SPAN to be extensible so that it can be used in short and long-term deployments of varying scales. A small-scale deployment, which may include just one CompactRIO and a number of motes (for distributed sensing), is a low-cost solution for individual scientists. It can be easily customized and deployed at different locations. Such a system can also be extended to support a small team of scientists that collect data in the same area. In comparison, a large-scale deployment can be achieved by deploying multiple CompactRIOs at many different locations, which are then connected through the Internet. Such an infrastructure is likely to be shared by a large number of scientists, and can provide access to a rich set of environmental sensors with geographically diverse settings.